IEEE 2017 – IEEE has available to assist engineering students with their final-year projects.IEEE – IEEE Resources for Final-Year Engineering Projects. The Institute of Electrical and Electronics Engineers (IEEE) is a professional association with its corporate office in New York City.

Privacy-Preserving Multikeyword Similarity Search Over
Outsourced Cloud Data

Introduction:
The amount of data generated by individuals and enterprises is rapidly increasing. With the emerging cloud computing paradigm, the data and corresponding complex management tasks can be outsourced to the cloud for the management flexibility and cost savings. Unfortunately, as the data could be sensitive, the direct data outsourcing would have the problem of privacy leakage. The encryption can be used, before the data outsourcing, with the concern that the operations can still be accomplished by the cloud. We consider the multikeyword similarity search over outsourced cloud data. In particular, with the consideration of the text data only, multiple keywords are specified by the user. The cloud returns the files containing more than a threshold number of input keywords or similar keywords, where the similarity here is defined according to the edit distance metric. We propose three solutions, where blind signature provides the user access privacy, and a novel use of Bloom filter’s bit pattern provides the speedup of search task at the cloud side. Our final design to achieve the search is secure against insider threats and efficient in terms of the search time at the cloud side. Performance evaluation and analysis are used to demonstrate the practicality of our proposed solutions.

Reference IEEE paper:
“Privacy-Preserving Multikeyword Similarity Search Over Outsourced Cloud Data” , IEEE SYSTEMS JOURNAL, 2017.

Unique ID -SBI1020

DomainCLOUD COMPUTING

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Privacy Protection based Access Control Scheme in Cloud-based
Services

Introduction:
With the rapid development of the computer technology, cloud-based services have become a hot topic. Cloud based services not only provide users with convenience, but also bring many security issues. Therefore, the study of access control scheme to protect users’ privacy in cloud environment is of great significance. In this paper, we present an access control system with privilege separation based on privacy protection (PS-ACS). In the PS-ACS scheme, we divide the users into personal domain (PSD) and public domain (PUD) logically. In the PSD, we set read and write access permissions for users respectively. The Key-Aggregate Encryption (KAE) is exploited to implement the read access permission which improves the access efficiency. A high degree of patient privacy is guaranteed simultaneously by exploiting an Improved Attribute-based Signature (IABS) which can determine the users’ write access. For the users of PUD, a hierarchical attribute-based encryption (HABE) is applied to avoid the issues of single point of failure and complicated key distribution. Function and performance testing result shows that the PS-ACS scheme can achieve privacy protection in cloud based services.

Reference IEEE paper :
“Privacy Protection based Access Control Scheme in Cloud-based Services”, IEEE 2017.

Unique ID -SBI1019

DomainCLOUD COMPUTING

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Identity-Based Encryption with Cloud Revocation Authority and
Its Applications

Introduction:
Identity-based encryption (IBE) is a public key crypto system and eliminates the demands of public key infrastructure (PKI) and certificate administration in conventional public key settings. Due to the absence of PKI, the revocation problem is a critical issue in IBE settings. Several revocable IBE schemes have been proposed regarding this issue. Quite recently, by embedding an outsourcing computation technique into IBE, Li et al. proposed a revocable IBE scheme with a key-update cloud service provider (KU-CSP). However, their scheme has two shortcomings. One is that the computation and communication costs are higher than previous revocable IBE schemes. The other shortcoming is lack of scalability in the sense that the KU-CSP must keep a secret value for each user. In the article, we propose a new revocable IBE scheme with a cloud revocation authority (CRA) to solve the two shortcomings, namely, the performance is significantly improved and the CRA holds only a system secret for all the users. For security analysis, we demonstrate that the proposed scheme is semantically secure under the decisional bilinear Diffie-Hellman (DBDH) assumption. Finally, we extend the proposed revocable IBE scheme to present a CRA-aided authentication scheme with period-limited privileges for managing a large number of various cloud services.

Reference IEEE paper:
“Identity-Based Encryption with Cloud Revocation Authority and Its Applications”, IEEE TRANS. CLOUD COMPUTING 2017.

Unique ID -SBI1014

DomainCLOUD COMPUTING

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Fast Phrase Search for Encrypted Cloud Storage

Introduction:
Cloud computing has generated much interest in the research community in recent years for its many advantages, but has also raise security and privacy concerns. The storage and access of confidential documents have been identified as one of the central problems in the area. In particular, many researchers investigated solutions to search over encrypted documents stored on remote cloud servers. While many schemes have been proposed to perform conjunctive keyword search, less attention has been noted on more specialized searching techniques. In this paper, we present a phrase search technique based on Bloom filters that is significantly faster than existing solutions, with similar or better storage and communication cost. Our technique uses a series of n-gram filters to support the functionality. The scheme exhibits a trade-off between storage and false positive rate, and is adaptable to defend against inclusion-relation attacks. A design approach based on an application’s target false positive rate is also described.

Reference IEEE paper:
“Fast Phrase Search for Encrypted Cloud Storage”, IEEE Transactions on Cloud Computing, 2017.

Unique ID -SBI1012

DomainCLOUD COMPUTING

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Efficient and Expressive Keyword Search Over Encrypted Data in
Cloud

Introduction:
Searchable encryption allows a cloud server to conduct keyword search over encrypted data on behalf of the data users without learning the underlying plain texts. However, most existing searchable encryption schemes only support single or conjunctive keyword search, while a few other schemes that are able to perform expressive keyword search are computationally inefficient since they are built from bilinear pairings over the composite-order groups. In this paper, we propose an expressive public-key searchable encryption scheme in the prime-order groups, which allows keyword search policies (i.e., predicates, access structures) to be expressed in conjunctive, disjunctive or any monotonic Boolean formulas and achieves significant performance improvement over existing schemes. We formally define its security, and prove that it is selectively secure in the standard model. Also, we implement the proposed scheme using a rapid prototyping tool called Charm, and conduct several experiments to evaluate it performance. The results demonstrate that our scheme is much more efficient than the ones built over the composite-order groups.

Reference IEEE paper:
“Efficient and Expressive Keyword Search Over Encrypted Data in Cloud”, IEEE Transactions on Dependable and Secure Computing, 2017.

Unique ID -SBI1011

DomainCLOUD COMPUTING

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Towards Detecting Compromised Accounts on Social Networks

Introduction :

Compromising social network accounts has become a profitable course of action for cybercriminals. By hijacking control of a popular media or business account, attackers can distribute their malicious messages or disseminate fake information to a large user base. The impacts of these incidents range from a tarnished reputation to multi-billion dollar monetary losses on financial markets. In our previous work, we demonstrated how we can detect large-scale compromises (i.e., so-called campaigns) of regular online social network users. In this work, we show how we can use similar techniques to identify compromises of individual high-profile accounts. High-profile accounts frequently have one characteristic that makes this detection reliable – they show consistent behaviour over time. We show that our system, were it deployed, would have been able to detect and prevent three real-world attacks against popular companies and news agencies. Furthermore, our system, in contrast to popular media, would not have fallen for a staged compromise instigated by a US restaurant chain for publicity reasons.

Reference IEEE paper:

“Towards Detecting Compromised Accounts on Social Networks”, IEEE Transactions on Dependable and Secure Computing, 2017.

Unique ID – SBI1073

Domain – SECURE COMPUTING

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NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media

Introduction:

Nowadays, a big part of people rely on available content in social media in their decisions (e.g. reviews and feedback on a topic or product). The possibility that anybody can leave a review provide a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this study, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review datasets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features help us to obtain better results in terms of different metrics experimented on real-world review datasets from Yelp and Amazon websites. The results show that NetSpam outperforms the existing methods and among four categories of features; including review-behavioral, user-behavioral, review linguistic, user-linguistic, the first type of features performs better than the other categories.

Reference IEEE paper:

“NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media”, IEEE Transactions on Information Forensics and Security, 2017.

Unique ID – SBI1074

Domain – INFORMATION FORENSICS & SECURITY

 

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Preventing Distributed Denial of Service Flooding Attacks with Dynamic Path Identifiers

Introduction:

In recent years, there are increasing interests in using path identifiers (PIDs) as inter-domain routing objects. However, the PIDs used in existing approaches are static, which makes it easy for attackers to launch distributed denial-of service (DDoS) flooding attacks. To address this issue, in this paper, we present the design, implementation, and evaluation of D-PID, a framework that uses PIDs negotiated between neighbouring domains as inter-domain routing objects. In DPID, the PID of an inter-domain path connecting two domains is kept secret and changes dynamically. We describe in detail how neighbouring domains negotiate PIDs, how to maintain ongoing communications when PIDs change. We build a 42-node prototype comprised by six domains to verify D-PID’s feasibility and conduct extensive simulations to evaluate its effectiveness and cost. The results from both simulations and experiments show that D-PID can effectively prevent DDoS attacks.

Reference IEEE paper:

“Preventing Distributed Denial-of-Service Flooding Attacks with Dynamic Path Identifiers”, IEEE TRANSACTIONS ON INFORMATION AND FORENSICS SECURITY, 2017.

Unique ID – SBI1075

Domain – INFORMATION FORENSICS & SECURITY

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Twitter Trends Manipulation: A First Look Inside the Security of Twitter Trending

Introduction:

Twitter trends, a timely updated set of top terms in Twitter, have the ability to affect the public agenda of the community and have attracted much attention. Unfortunately, in the wrong hands, Twitter trends can also be abused to mislead people. In this paper, we attempt to investigate whether Twitter trends are secure from the manipulation of malicious users. We collect more than 69 million tweets from 5 million accounts. Using the collected tweets, we first conduct a data analysis and discover evidence of Twitter trend manipulation. Then, we study at the topic level and infer the key factors that can determine whether a topic starts trending due to its popularity, coverage, transmission, potential coverage, or reputation. What we find is that except for transmission, all of factors above are closely related to trending. Finally, we further investigate the trending manipulation from the perspective of compromised and fake accounts and discuss countermeasures.

Reference IEEE paper:

“Twitter Trends Manipulation: A First Look Inside the Security of Twitter Trending”, IEEE Transactions on Information Forensics and Security, 2017.

Unique ID – SBI1076

DomainINFORMATION FORENSICS & SECURITY

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A Distributed Publisher-Driven Secure Data Sharing Scheme for Information-Centric IoT

Introduction:

In Information-Centric Internet of Things (ICIoT), IoT data can be cached throughout a network for close data copy retrievals. Such a distributed data caching environment, however, poses a challenge to flexible authorization in the network. To address this challenge, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been identified as a promising approach. However in the existing CP-ABE scheme, publishers need to retrieve attributes from a centralized server for encrypting data, which leads to high communication overhead. To solve this problem, we incorporate CP-ABE and propose a novel Distributed Publisher-driven secure Data sharing for ICIoT (DPD-ICIoT) to enable only authorized users to retrieve IoT data from distributed cache. In DPDICIoT, newly introduced Attribute Manifest (AM) is cached in the network, through which publishers can retrieve the attributes from nearby copy holders instead of a centralized attribute server. In addition, a key chain mechanism is utilized for efficient cryptographic operations, and an Automatic Attribute Self-update Mechanism (AASM) is proposed to enable fast updates of attributes without querying centralized servers. According to the performance evaluation, DPD-ICIoT achieves lower bandwidth cost compared to the existing CPABE scheme.

Reference IEEE paper:

“A Distributed Publisher-Driven Secure Data Sharing Scheme for Information-Centric IoT”, THE IEEE IOT JOURNAL, 2017.

Unique ID – SBI1077

Domain – INTERNET OF THINGS (IoT)

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An Efficient and Fine-grained Big Data Access Control Scheme with Privacy-preserving Policy

Introduction:

How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-Policy Attribute based Encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel Attribute Bloom Filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any LSSS access policy without employing much overhead.

Reference IEEE paper:

“An Efficient and Fine-grained Big Data Access Control Scheme with Privacy-preserving Policy”, IEEE Internet of Things Journal, 2017.

Unique ID – SBI1078

Domain – INTERNET OF THINGS (IoT)

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Efficient and Privacy preserving Polygons Spatial Query Framework for Location-based Services

Introduction

With the pervasiveness of mobile devices and the development of wireless communication technique, location-based services (LBS) have made our life more convenient, and the polygons spatial query, which can provide more flexible LBS, has attracted considerable interest recently. However, the flourish of polygons spatial query still faces many challenges including the query information privacy. In this paper, we present an efficient and privacy-preserving polygons spatial query framework for location-based services, called Polaris. With Polaris, the LBS provider outsources the encrypted LBS data to cloud server, and the registered user can query any polygon range to get accurate LBS results without divulging his/her query information to the LBS provider and cloud server. Specifically, an efficient special polygons spatial query algorithm (SPSQ) over ciphertext is constructed, based on an improved homomorphic encryption technology over composite order group. With SPSQ, Polaris can search outsourced encrypted LBS data in cloud server by the encrypted request, and respond the encrypted polygons spatial query results accurately. Detailed security analysis shows that the proposed Polaris can resist various known security threats. In addition, performance evaluations via implementing Polaris on smartphone and workstation with real LBS dataset demonstrate Polaris’ effectiveness in term of real environment.

Reference IEEE paper:

“Efficient and Privacy-preserving Polygons Spatial Query Framework for Location-based Services”, IEEE INTERNET OF THINGS JOURNAL, 2017.

Unique ID – SBI1079

Domain – INTERNET OF THINGS (IoT)

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Follow But No Track: Privacy Preserved Profile Publishing in Cyber-Physical Social Systems

Introduction:

Due to the close correlation with individual’s physical features and status, the adoption of Cyber-Physical Social Systems (CPSSs) has been inevitably hindered by users’ privacy concerns. Such concerns keep growing as our bile devices have more embedded sensors, while the existing countermeasures only provide incapable and limited privacy preservation for sensitive physical information. Therefore, we propose a novel privacy preservation framework for CPSSs.We formulate both the privacy concerns and user expectations in CPSSs based on real-world knowledge. We also design a corresponding data publishing mechanism for users. It regulates the publishing behaviors to hide sensitive physical profiles. Meanwhile, the published data retain comprehensive social profiles for users. Our analysis demonstrates that the mechanism achieves a local maximized performance on the aspect published data size. The experiment results towards real datasets reveals that the performance is comparable to the global optimal one.

Reference IEEE paper:

“Follow But No Track: Privacy Preserved Profile Publishing in Cyber-Physical Social Systems”, IEEE Internet of Things Journal, 2017.

Unique ID – SBI1080

Domain – INTERNET OF THINGS (IoT)

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A robust reputation management mechanism in the federated
cloud.

Introduction:
In the Infrastructure as a Service (IaaS) paradigm of cloud computing, computational resources are available for rent. Although it offers a cost efficient solution to virtual network requirements, low trust on the rented computational resources prevents users from using it. To reduce the cost, computational resources are shared, i.e., there exists multi-tenancy. As the communication channels and other computational resources are shared, it creates security and privacy issues. A user may not identify a trustworthy co-tenant as the users are anonymous. The user depends on the Cloud Provider (CP) to assign trustworthy co-tenants. But, it is in the CP’s interest that it gets maximum utilization of its resources. Hence, it allows maximum co-tenancy irrespective of the behaviours of users. In this paper, we propose a robust reputation management mechanism that encourages the CPs in a federated cloud to differentiate between good and malicious users and assign resources in such a way that they do not share resources. We show the correctness and the efficiency of the proposed reputation management system using analytical and experimental analysis.

Reference IEEE paper :
“A robust reputation management mechanism in the federated cloud”, IEEE Transactions on Cloud Computing, 2017.

Unique ID -SBI1006

DomainCLOUD COMPUTING

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A Modified Hierarchical Attribute-Based Encryption Access Control Method for Mobile Cloud Computing

Introduction:

Cloud computing is an Internet-based computing pattern through which shared resources are provided to devices on demand. Its an emerging but promising paradigm to integrating mobile devices into cloud computing, and the integration performs in the cloud based hierarchical multi-user data-shared environment. With integrating into cloud computing, security issues such as data confidentiality and user authority may arise in the mobile cloud computing system, and it is concerned as the main constraints to the developments of mobile cloud computing. In order to provide safe and secure operation, a hierarchical access control method using modified hierarchical attribute-based encryption (M-HABE) and a modified three-layer structure is proposed in this paper. In a specific mobile cloud computing model, enormous data which may be from all kinds of mobile devices, such as smart phones, functioned phones and PDAs and so on can be controlled and monitored by the system, and the data can be sensitive to unauthorized third party and constraint to legal users as well. The novel scheme mainly focuses on the data processing, storing and accessing, which is designed to ensure the users with legal authorities to get corresponding classified data and to restrict illegal users and unauthorized legal users get access to the data, which makes it extremely suitable for the mobile cloud computing paradigms.

Reference IEEE paper :

“A Modified Hierarchical Attribute-Based Encryption Access Control Method for Mobile Cloud Computing”, IEEE Transactions on Cloud Computing, 2017.

Unique ID – SBI1003

Domain – CLOUD COMPUTING

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A New Service Mechanism for Profit Optimizations of a Cloud
Provider and Its Users

Introduction:
In this paper, we try to design a service mechanism for profit optimizations of both a cloud provider and its multiple users. We consider the problem from a game theoretic perspective and characterize the relationship between the cloud provider and its multiple users as a Stackelberg game, in which the strategies of all users are subject to that of the cloud provider. The cloud provider tries to select and provision appropriate servers and configure a proper request allocation strategy to reduce energy cost while satisfying its cloud users at the same time. We approximate its servers selection space by adding a controlling parameter and configure an optimal request allocation strategy. For each user, we design a utility function which combines the net profit with time efficiency and try to maximize its value under the strategy of the cloud provider. We formulate the competitions among all users as a generalized Nash equilibrium problem (GNEP). We solve the problem by employing variational inequality (VI) theory and prove that there exists a generalized Nash equilibrium solution set for the formulated GNEP. Finally, we propose an iterative algorithm (IA), which characterizes the whole process of our proposed service mechanism. We conduct some numerical calculations to verify our theoretical analyses. The experimental results show that our IA algorithm can benefit both of a cloud provider and its multiple users by configuring proper strategies.

Reference IEEE paper :
“A New Service Mechanism for Profit Optimizations of a Cloud Provider and Its Users”, IEEE Transactions on Cloud Computing, 2017.

Unique ID -SBI1004

DomainCLOUD COMPUTING

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A Novel Efficient Remote Data Possession Checking Protocol in
Cloud Storage

Introduction:
As an important application in cloud computing, cloud storage offers user scalable, flexible and high quality data storage and computation services. A growing number of data owners choose to outsource data files to the cloud. Because cloud storage servers are not fully trustworthy, data owners need dependable means to check the possession for their files outsourced to remote cloud servers. To address this crucial problem, some remote data possession checking (RDPC) protocols have been presented. But many existing schemes have vulnerabilities in efficiency or data dynamics. In this paper, we provide a new efficient RDPC protocol based on homomorphic hash function. The new scheme is provably secure against forgery attack, replace attack and replay attack based on a typical security model. To support data dynamics, an operation record table (ORT) is introduced to track operations on file blocks. We further give a new optimized implementation for the ORT which makes the cost of accessing ORT nearly constant. Moreover, we make the comprehensive performance analysis which shows that our scheme has advantages in computation and communication costs. Prototype implementation and experiments exhibit that the scheme is feasible for real applications.

Reference IEEE paper :
“A Novel Efficient Remote Data Possession Checking Protocol in Cloud Storage”, IEEE 2017

Unique ID -SBI1005

DomainCLOUD COMPUTING

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Search Rank Fraud and Malware Detection in Google Play

Introduction :

Fraudulent behaviours in Google Play, the most popular Android app market, fuel search rank abuse and malware proliferation. To identify malware, previous work has focused on app executable and permission analysis. In this paper, we introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. FairPlay correlates review activities and uniquely combines detected review relations with linguistic and behavioural signals gleaned from Google Play app data (87K apps, 2.9M reviews, and 2.4M reviewers, collected over half a year), in order to identify suspicious apps. FairPlay achieves over 95% accuracy in classifying gold standard datasets of malware, fraudulent and legitimate apps. We show that 75% of the identified malware apps engage in search rank fraud. FairPlay discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology. FairPlay also helped the discovery of more than 1,000 reviews, reported for 193 apps, that reveal a new type of “coercive” review campaign: users are harassed into writing positive reviews, and install and review other apps.

Reference IEEE paper :

“Search Rank Fraud and Malware Detection in Google Play”, IEEE Transactions on Knowledge and Data Engineering, 2017.

Unique ID – SBI1072

Domain – SECURE COMPUTING

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ProGuard Detecting Malicious Accounts in Social-Network-Based Online Promotions

Introduction :

Online social networks gradually integrate financial capabilities by enabling the usage of real and virtual currency. They serve as new platforms to host a variety of business activities such as online promotion events, where users can possibly get virtual currency as rewards by participating such events. Both OSNs and business partners are significantly concerned when attackers instrument a set of accounts to collect virtual currency from these events, which make these events ineffective and result in significant financial loss. It becomes of great importance to proactively detecting these malicious accounts before the online promotion activities and subsequently decrease their priority to be rewarded. In this paper, we propose a novel system, namely ProGuard, to accomplish this objective by systematically integrating features that characterize accounts from three perspectives including their general behaviors, their recharging patterns, and the usage of their currency. We have performed extensive experiments based on data collected from Tencent QQ, a global leading OSN with built-in financial management activities. Experimental results have demonstrated that our system can accomplish a high detection rate of 96.67% at a very low false positive rate of 0.3%.

Reference IEEE paper :

“ProGuard: Detecting Malicious Accounts in Social-Network-Based Online Promotions”, IEEE Access, 2017.

Unique ID -SBI1171

Domain – SECURE COMPUTING

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A Lightweight Secure Data Sharing Scheme for Mobile Cloud Computing

Introduction :

With the popularity of cloud computing, mobile devices can store/retrieve personal data from anywhere at any time. Consequently, the data security problem in mobile cloud becomes more and more severe and prevents further development of mobile cloud. There are substantial studies that have been conducted to improve the cloud security. However, most of them are not applicable for mobile cloud since mobile devices only have limited computing resources and power. Solutions with low computational overhead are in great need for mobile cloud applications. In this paper, we propose a lightweight data sharing scheme (LDSS) for mobile cloud computing. It adopts CP-ABE, an access control technology used in normal cloud environment, but changes the structure of access control tree to make it suitable for mobile cloud environments. LDSS moves a large portion of the computational intensive access control tree transformation in CP-ABE from mobile devices to external proxy servers. Furthermore, to reduce the user revocation cost, it introduces attribute description fields to implement lazy-revocation, which is a thorny issue in program based CP-ABE systems. The experimental results show that LDSS can effectively reduce the overhead on the mobile device side when users are sharing data in mobile cloud environments.

Reference IEEE paper :

“A Lightweight Secure Data Sharing Scheme for Mobile Cloud Computing”, IEEE Transactions on Cloud Computing, 2017.

Unique ID -SBI1002

Domain – CLOUD COMPUTING

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Privacy Preserving Selective Aggregation of Online User Behaviour Data

Introduction :

Tons of online user behaviour data are being generated every day on the booming and ubiquitous Internet. Growing efforts have been devoted to mining the abundant behaviour data to extract valuable information for research purposes or business interests. However, online users’ privacy is thus under the risk of being exposed to third-parties. The last decade has witnessed a body of research works trying to perform data aggregation in a privacy-preserving way. Most of existing methods guarantee strong privacy protection yet at the cost of very limited aggregation operations, such as allowing only summation, which hardly satisfies the need of behaviour analysis. In this paper, we propose a scheme PPSA, which encrypts users’ sensitive data to prevent privacy disclosure from both outside analysts and the aggregation service provider, and fully supports selective aggregate functions for online user behaviour analysis while guaranteeing differential privacy. We have implemented our method and evaluated its performance using a trace-driven evaluation based on a real online behaviour dataset. Experiment results show that our scheme effectively supports both overall aggregate queries and various selective aggregate queries with acceptable computation and communication overheads.

Reference IEEE paper :

“Privacy-Preserving Selective Aggregation of Online User Behaviour Data” , IEEE Transactions on Computers, 2017.

Unique ID – SBI1070

Domain – SECURE COMPUTING

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Control of Photo Sharing on Online Social Networks : My Privacy My Decision

Introduction:

Photo sharing is an attractive feature which popularizes Online Social Networks (OSNs). Unfortunately, it may leak users privacy if they are allowed to post, comment, and tag a photo freely. In this paper, we attempt to address this issue and study the scenario when a user shares a photo containing individuals other than himself/herself (termed co-photo for short). To prevent possible privacy leakage of a photo, we design a mechanism to enable each individual in a photo be aware of the posting activity and participate in the decision making on the photo posting. For this purpose, we need an efficient facial recognition (FR) system that can recognize everyone in the photo. However, more demanding privacy setting may limit the number of the photos publicly available to train the FR system. To deal with this dilemma, our mechanism attempts to utilize users’ private photos to design a personalized FR system specifically trained to differentiate possible photo co-owners without leaking their privacy. We also develop a distributed consensus based method to reduce the computational complexity and protect the private training set. We show that our system is superior to other possible approaches in terms of recognition ratio and efficiency. Our mechanism is implemented as a proof of concept Android application on Facebook’s platform.

Control Photo Sharing on Online Social Networks : My Privacy My Decision

Reference IEEE paper :

“My Privacy My Decision: Control of Photo Sharing on Online Social Networks”, IEEE Transactions on Dependable and Secure Computing, 2017.

Unique ID – SBI1069

Domain – SECURE COMPUTING

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Image Reranking based on Topic Diversity

INTRODUCTION:

Social media sharing websites allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval. Tag-based image search is an important method to find images shared by users in social networks. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a topic diverse ranking approach for tag-based image retrieval with the consideration of promoting the topic coverage performance. First, we construct a tag graph based on the similarity between each tag. Then community detection method is conducted to mine the topic community of each tag. After that, inter-community and intra-community ranking are introduced to obtain the final retrieved results. In the inter-community ranking process, an adaptive random walk model is employed to rank the community based on the multi-information of each topic community. Besides, we build an inverted index structure for images to accelerate the searching process. Experimental results on Flickr dataset and NUS-Wide datasets show the effectiveness of the proposed approach.

REFERENCE IEEE PAPER:

Image Re-ranking based on Topic Diversity”,  IEEE Transactions on Image Processing, 2017

Unique ID -SBI1085

DomainIMAGE PROCESSING

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Automatic Generation of Social Event Storyboard from Image Click-through Data

Introduction :

Recent studies have shown that a noticeable percentage of web search traffic is about social events. While traditional websites can only show human-edited events, in this paper we present a novel system to automatically detect events from search log data and generate storyboards where the events are arranged chronologically. We chose image search log as the resource for event mining, as search logs can directly reflect people’s interests. To discover events from log data, we present a Smooth Nonnegative Matrix Factorization framework (SNMF) which combines the information of query semantics, temporal correlations, search logs and time continuity. Moreover, we consider the time factor an important element since different events will develop in different time tendencies. In addition, to provide a media-rich and visually appealing storyboard, each event is associated with a set of representative photos arranged along a timeline. These relevant photos are automatically selected from image search results by analyzing image content features. We use celebrities as our test domain, which takes a large percentage of image search traffics. Experiments consisting of web search traffic on 200 celebrities, for a period of six months, show very encouraging results compared with handcrafted editorial storyboards.

Reference IEEE paper:

Automatic Generation of Social Event Storyboard from Image Click-through Data”, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2017.

Unique ID -SBI1084

DomainImage Processing

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Social Q&A: An Online Social Network Based Question and Answer System

Introduction :

Question and Answer (Q&A) systems play a vital role in our daily life for information and knowledge sharing. Users post questions and pick questions to answer in the system. Due to the rapidly growing user population and the number of questions, it is unlikely for a user to stumble upon a question by chance that (s)he can answer. Also, altruism does not encourage all users to provide answers, not to mention high quality answers with a short answer wait time. The primary objective of this paper is to improve the performance of Q&A systems by actively forwarding questions to users who are capable and willing to answer the questions. To this end, we have designed and implemented Social Q&A, an online social network based Q&A system. Social Q&A leverages the social network properties of common-interest and mutual-trust friend relationship to identify an asker through friendship who are most likely to answer the question, and enhance the user security. We also improve Social Q&A with security and efficiency enhancements by protecting user privacy and identifies, and retrieving answers automatically for recurrent questions. We describe the architecture and algorithms, and conducted comprehensive large-scale simulation to evaluate Social Q&A in comparison with other methods. Our results suggest that social networks can be leveraged to improve the answer quality and asker’s waiting time. We also implemented a real prototype of Social Q&A, and analyze the Q&A behavior of real users and questions from a small-scale real-world Social Q&A system.

Reference IEEE paper :

“SocialQ&A: An Online Social Network Based Question and Answer System”, IEEE Transactions on Big Data, 2017.

Unique ID – SBI1083

DomainBIG DATA

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SPFM: Scalable and Privacy Preserving Friend Matching in Mobile Cloud

Introduction:

Profile (e.g., contact list, interest, mobility) matching is more than important for fostering the wide use of mobile social networks. The social networks such as Facebook, Line or We-chat recommend the friends for the users based on users personal data such as common contact list or mobility traces. However, outsourcing users’ personal information to the cloud for friend matching will raise a serious privacy concern due to the potential risk of data abusing. In this study, we propose a novel Scalable and Privacy-preserving Friend Matching protocol, or SPFM in short, which aims to provide a scalable friend matching and recommendation solutions without revealing the users personal data to the cloud. Different from the previous works which involves multiple rounds of protocols, SPFM presents a scalable solution which can prevent honest-but-curious mobile cloud from obtaining the original data and support the friend matching of multiple users simultaneously. We give detailed feasibility and security analysis on SPFM and its accuracy and security have been well demonstrated via extensive simulations. The result show that our scheme works even better when original data is large.

SPFM: Scalable and Privacy Preserving Friend Matching in Mobile Cloud

Reference IEEE paper :

“SPFM: Scalable and Privacy-preserving Friend Matching in Mobile Cloud”, IEEE Internet of Things Journal, 2017.

Unique ID – SBI1081

DomainInternet of Things (IOT)

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Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

Introduction :

Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud. Attribute-based encryption (ABE) has been widely used in cloud computing where a data provider outsources his/her encrypted data to a cloud service provider, and can share the data with users possessing specific credentials (or attributes). However, the standard ABE system does not support secure deduplication, which is crucial for eliminating duplicate copies of identical data in order to save storage space and network bandwidth. In this paper, we present an attribute-based storage system with secure deduplication in a hybrid cloud setting, where a private cloud is responsible for duplicate detection and a public cloud manages the storage. Compared with the prior data deduplication systems, our system has two advantages. Firstly, it can be used to confidentially share data with users by specifying access policies rather than sharing decryption keys. Secondly, it achieves the standard notion of semantic security for data confidentiality while existing systems only achieve it by defining a weaker security notion. In addition, we put forth a methodology to modify a ciphertext over one access policy into ciphertexts of the same plaintext but under other access policies without revealing the underlying plaintext.

Reference IEEE paper:

“Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud”, IEEE Transactions on Big Data, 2017.

Unique ID – SBI1082

DomainBIG DATA

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Topic Rehotting Prediction in Online Social Networks

Topic rehotting prediction is popular technique in social networks. It is really popular to detect hot topics, which can benefit many tasks including topic recommendations, the guidance of public opinions, and so on. However, in some cases, people may want to know when to re-hot a topic, i.e., make the topic popular again. In this paper, we address this issue by introducing a temporal User Topic Participation (UTP) model which models users behaviours of posting messages. The UTP model takes into account users interests, friend-circles, and unexpected events in online social networks. Also, it considers the continuous temporal modelling of topics, since topics are changing continuously over time. Furthermore, a weighting scheme is proposed to smooth the fluctuations in topic re-hotting prediction. Finally, experimental results conducted on real world data sets demonstrate the effectiveness of our proposed models and topic re-hotting prediction methods.

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